Metadata-Version: 1.1
Name: bigml
Version: 0.8.0
Summary: An open source binding to BigML.io, the public BigML API
Home-page: https://bigml.com/developers
Author: The BigML Team
Author-email: bigml@bigml.com
License: http://www.apache.org/licenses/LICENSE-2.0
Download-URL: https://github.com/bigmlcom/python
Description: BigML Python Bindings
        =====================
        
        `BigML <https://bigml.com>`_ makes machine learning easy by taking care
        of the details required to add data-driven decisions and predictive
        power to your company. Unlike other machine learning services, BigML
        creates
        `beautiful predictive models <https://bigml.com/gallery/models>`_ that
        can be easily understood and interacted with.
        
        These BigML Python bindings allow you to interact with BigML.io, the API
        for BigML. You can use it to easily create, retrieve, list, update, and
        delete BigML resources (i.e., sources, datasets, models and,
        predictions).
        
        This module is licensed under the `Apache License, Version
        2.0 <http://www.apache.org/licenses/LICENSE-2.0.html>`_.
        
        Support
        -------
        
        Please report problems and bugs to our `BigML.io issue
        tracker <https://github.com/bigmlcom/io/issues>`_.
        
        Discussions about the different bindings take place in the general
        `BigML mailing list <http://groups.google.com/group/bigml>`_. Or join us
        in our `Campfire chatroom <https://bigmlinc.campfirenow.com/f20a0>`_.
        
        Requirements
        ------------
        
        Python 2.6 and Python 2.7 are currently supported by these bindings.
        
        The only mandatory third-party dependencies are the
        `requests <https://github.com/kennethreitz/requests>`_,
        `poster <http://atlee.ca/software/poster/#download>`_ and
        `unidecode <http://pypi.python.org/pypi/Unidecode/#downloads>`_ libraries. These
        libraries are automatically installed during the setup.
        
        The bindings will also use ``simplejson`` if you happen to have it
        installed, but that is optional: we fall back to Python's built-in JSON
        libraries is ``simplejson`` is not found.
        
        Installation
        ------------
        
        To install the latest stable release with
        `pip <http://www.pip-installer.org/>`_::
        
            $ pip install bigml
        
        You can also install the development version of the bindings directly
        from the Git repository::
        
            $ pip install -e git://github.com/bigmlcom/python.git#egg=bigml_python
        
        Importing the module
        --------------------
        
        To import the module::
        
            import bigml.api
        
        Alternatively you can just import the BigML class::
        
            from bigml.api import BigML
        
        Authentication
        --------------
        
        All the requests to BigML.io must be authenticated using your username
        and `API key <https://bigml.com/account/apikey>`_ and are always
        transmitted over HTTPS.
        
        This module will look for your username and API key in the environment
        variables ``BIGML_USERNAME`` and ``BIGML_API_KEY`` respectively. You can
        add the following lines to your ``.bashrc`` or ``.bash_profile`` to set
        those variables automatically when you log in::
        
            export BIGML_USERNAME=myusername
            export BIGML_API_KEY=ae579e7e53fb9abd646a6ff8aa99d4afe83ac291
        
        With that environment set up, connecting to BigML is a breeze::
        
            from bigml.api import BigML
            api = BigML()
        
        Otherwise, you can initialize directly when instantiating the BigML
        class as follows::
        
            api = BigML('myusername', 'ae579e7e53fb9abd646a6ff8aa99d4afe83ac291')
        
        Also, you can initialize the library to work in the Sandbox environment by
        passing the parameter ``dev_mode``::
        
            api = BigML(dev_mode=True)
        
        Quick Start
        -----------
        
        Imagine that you want to use `this csv
        file <https://static.bigml.com/csv/iris.csv>`_ containing the `Iris
        flower dataset <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_ to
        predict the species of a flower whose ``sepal length`` is ``5`` and
        whose ``sepal width`` is ``2.5``. A preview of the dataset is shown
        below. It has 4 numeric fields: ``sepal length``, ``sepal width``,
        ``petal length``, ``petal width`` and a categorical field: ``species``.
        By default, BigML considers the last field in the dataset as the
        objective field (i.e., the field that you want to generate predictions
        for).
        
        ::
        
            sepal length,sepal width,petal length,petal width,species
            5.1,3.5,1.4,0.2,Iris-setosa
            4.9,3.0,1.4,0.2,Iris-setosa
            4.7,3.2,1.3,0.2,Iris-setosa
            ...
            5.8,2.7,3.9,1.2,Iris-versicolor
            6.0,2.7,5.1,1.6,Iris-versicolor
            5.4,3.0,4.5,1.5,Iris-versicolor
            ...
            6.8,3.0,5.5,2.1,Iris-virginica
            5.7,2.5,5.0,2.0,Iris-virginica
            5.8,2.8,5.1,2.4,Iris-virginica
        
        You can easily generate a prediction following these steps::
        
            from bigml.api import BigML
        
            api = BigML()
        
            source = api.create_source('./data/iris.csv')
            dataset = api.create_dataset(source)
            model = api.create_model(dataset)
            prediction = api.create_prediction(model, {'sepal length': 5, 'sepal width': 2.5})
        
        You can then print the prediction using the ``pprint`` method::
        
            >>> api.pprint(prediction)
            species for {"sepal width": 2.5, "sepal length": 5} is Iris-virginica
        
        Additional Information
        ----------------------
        
        We've just barely scratched the surface. For additional information, see
        the `full documentation for the Python
        bindings on Read the Docs <http://bigml.readthedocs.org>`_.
        Alternatively, the same documentation can be built from a local checkout
        of the source by installing `Sphinx <http://sphinx.pocoo.org>`_
        (``$ pip install sphinx``) and then running::
        
            $ cd docs
            $ make html
        
        Then launch ``docs/_build/html/index.html`` in your browser.
        
        How to Contribute
        -----------------
        
        Please follow the next steps:
        
          1. Fork the project on github.com.
          2. Create a new branch.
          3. Commit changes to the new branch.
          4. Send a `pull request <https://github.com/bigmlcom/python/pulls>`_.
        
        
        For details on the underlying API, see the
        `BigML API documentation <https://bigml.com/developers>`_.
        
        
        .. :changelog:
        
        History
        -------
        
        0.8.0 (2013-08-10)
        ~~~~~~~~~~~~~~~~~~
        
        - Adds text analysis local predict function
        - Modifies outputs for text analysis: rules, summary, python, hadoop
        
        0.7.5 (2013-08-22)
        ~~~~~~~~~~~~~~~~~~
        
        - Fixes temporarily problems in predictions for regression models and
          ensembles
        - Adds en-gb to the list of available locales, avoiding spurious warnings
        
        0.7.4 (2013-08-17)
        ~~~~~~~~~~~~~~~~~~
        
        - Changes warning logger level to info
        
        0.7.3 (2013-08-09)
        ~~~~~~~~~~~~~~~~~~
        
        - Adds fields method to retrieve only preferred fields
        - Fixes error message when no valid resource id is provided in check_resource
        
        0.7.2 (2013-07-04)
        ~~~~~~~~~~~~~~~~~~
        
        - Fixes check_resource method that was not using query-string data
        - Add list of models as argument in Ensemble constructor
        - MultiModel has BigML connection as a new optional argument
        
        0.7.1 (2013-06-19)
        ~~~~~~~~~~~~~~~~~~
        
        - Fixes Multimodel list_models method
        - Fixes check_resource method for predictions
        - Adds local configuration environment variable BIGML_DOMAIN replacing
          BIGML_URL and BIGML_DEV_URL
        - Refactors Ensemble and Model's predict method
        
        0.7.0 (2013-05-01)
        ~~~~~~~~~~~~~~~~~~
        
        - Adds splits in datasets to generate new datasets
        - Adds evaluations for ensembles
        
        0.6.0 (2013-04-27)
        ~~~~~~~~~~~~~~~~~~
        
        - REST API methods for model ensembles
        - New method returning the leaves of tree models
        - Improved error handling in GET methods
        
        0.5.2 (2013-03-03)
        ~~~~~~~~~~~~~~~~~~
        
        - Adds combined confidence to combined predictions
        - Fixes get_status for resources that have no status info
        - Fixes bug: public datasets, that should be downloadable, weren't
        
        0.5.1 (2013-02-12)
        ~~~~~~~~~~~~~~~~~~
        
        - Fixes bug: no status info in public models, now shows FINISHED status code
        - Adds more file-like objects (e.g. stdin) support in create_source input
        - Refactoring Fields pair method and Model predict method to increase
        - Adds some more locale aliases
        
        0.5.0 (2013-01-16)
        ~~~~~~~~~~~~~~~~~~
        
        - Adds evaluation api functions
        - New prediction combination method: probability weighted
        - Refactors MultiModels lists of predictions into MultiVote
        - Multimodels partial predictions: new format
        
        0.4.8 (2012-12-21)
        ~~~~~~~~~~~~~~~~~~
        
        - Improved locale management
        - Adds new features to MultiModel to allow local batch predictions
        - Improved combined predictions
        - Adds local predictions options: plurality, confidence weighted
        
        0.4.7 (2012-12-06)
        ~~~~~~~~~~~~~~~~~~
        
        - Warning message to inform of locale default if verbose mode
        
        0.4.6 (2012-12-06)
        ~~~~~~~~~~~~~~~~~~
        
        - Fix locale code for windows
        
        0.4.5 (2012-12-05)
        ~~~~~~~~~~~~~~~~~~
        
        - Fix remote predictions for input data containing fields not included in rules
        
        0.4.4 (2012-12-02)
        ~~~~~~~~~~~~~~~~~~
        
        - Tiny fixes
        - Fix local predictions for input data containing fields not included in rules
        - Overall clean up
        
        0.4.3 (2012-11-07)
        ~~~~~~~~~~~~~~~~~~
        
        - A few tiny fixes
        - Multi models to generate predictions from multiple local models
        - Adds hadoop-python code generation to create local predictions
        
        0.4.2 (2012-09-19)
        ~~~~~~~~~~~~~~~~~~
        
        - Fix Python generation
        - Add a debug flag to log https requests and responses
        - Type conversion in fields pairing
        
        0.4.1 (2012-09-17)
        ~~~~~~~~~~~~~~~~~~
        
        - Fix missing distribution field in new models
        - Add new Field class to deal with BigML auto-generated ids
        - Add by_name flag to predict methods to avoid reverse name lookups
        - Add summarize method in models to generate class grouped printed output
        
        0.4.0 (2012-08-20)
        ~~~~~~~~~~~~~~~~~~
        
        - Development Mode
        - Remote Sources
        - Bigger files streamed with Poster
        - Asynchronous Uploading
        - Local Models
        - Local Predictions
        - Rule Generation
        - Python Generation
        - Overall clean up
        
        
        0.3.1 (2012-07-05)
        ~~~~~~~~~~~~~~~~~~
        
        - Initial release for the "andromeda" version of BigML.io.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Software Development :: Libraries :: Python Modules
